mirror of https://github.com/msberends/AMR.git
98 lines
4.5 KiB
R
98 lines
4.5 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/pca.R
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\name{pca}
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\alias{pca}
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\title{Principal Component Analysis (for AMR)}
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\usage{
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pca(
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x,
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...,
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retx = TRUE,
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center = TRUE,
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scale. = TRUE,
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tol = NULL,
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rank. = NULL
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)
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}
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\arguments{
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\item{x}{a \link{data.frame} containing \link{numeric} columns}
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\item{...}{columns of \code{x} to be selected for PCA, can be unquoted since it supports quasiquotation.}
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\item{retx}{a logical value indicating whether the rotated variables
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should be returned.}
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\item{center}{a logical value indicating whether the variables
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should be shifted to be zero centered. Alternately, a vector of
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length equal the number of columns of \code{x} can be supplied.
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The value is passed to \code{scale}.}
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\item{scale.}{a logical value indicating whether the variables should
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be scaled to have unit variance before the analysis takes
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place. The default is \code{FALSE} for consistency with S, but
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in general scaling is advisable. Alternatively, a vector of length
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equal the number of columns of \code{x} can be supplied. The
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value is passed to \code{\link{scale}}.}
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\item{tol}{a value indicating the magnitude below which components
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should be omitted. (Components are omitted if their
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standard deviations are less than or equal to \code{tol} times the
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standard deviation of the first component.) With the default null
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setting, no components are omitted (unless \code{rank.} is specified
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less than \code{min(dim(x))}.). Other settings for tol could be
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\code{tol = 0} or \code{tol = sqrt(.Machine$double.eps)}, which
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would omit essentially constant components.}
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\item{rank.}{optionally, a number specifying the maximal rank, i.e.,
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maximal number of principal components to be used. Can be set as
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alternative or in addition to \code{tol}, useful notably when the
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desired rank is considerably smaller than the dimensions of the matrix.}
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}
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\value{
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An object of classes \link{pca} and \link{prcomp}
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}
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\description{
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Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels, and automatic filtering on only suitable (i.e. non-empty and numeric) variables.
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}
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\details{
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The \code{\link[=pca]{pca()}} function takes a \link{data.frame} as input and performs the actual PCA with the \R function \code{\link[=prcomp]{prcomp()}}.
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The result of the \code{\link[=pca]{pca()}} function is a \link{prcomp} object, with an additional attribute \code{non_numeric_cols} which is a vector with the column names of all columns that do not contain \link{numeric} values. These are probably the groups and labels, and will be used by \code{\link[=ggplot_pca]{ggplot_pca()}}.
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}
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\section{Stable Lifecycle}{
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\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:"5"} \cr}
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The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
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If the unlying code needs breaking changes, they will occur gradually. For example, an argument will be deprecated and first continue to work, but will emit a message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
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}
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\section{Read more on Our Website!}{
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On our website \url{https://msberends.github.io/AMR/} you can find \href{https://msberends.github.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR data analysis, the \href{https://msberends.github.io/AMR/reference/}{complete documentation of all functions} and \href{https://msberends.github.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
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}
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\examples{
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# `example_isolates` is a data set available in the AMR package.
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# See ?example_isolates.
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\donttest{
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if (require("dplyr")) {
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# calculate the resistance per group first
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resistance_data <- example_isolates \%>\%
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group_by(order = mo_order(mo), # group on anything, like order
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genus = mo_genus(mo)) \%>\% # and genus as we do here;
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summarise_if(is.rsi, resistance) # then get resistance of all drugs
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# now conduct PCA for certain antimicrobial agents
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pca_result <- resistance_data \%>\%
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pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
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pca_result
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summary(pca_result)
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biplot(pca_result)
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ggplot_pca(pca_result) # a new and convenient plot function
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}
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}
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}
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